docstr: Classifier-based drift detector. The classifier is trained on a fraction of the combined
  reference and test data and drift is detected on the remaining data. To use all the data
  to detect drift, a stratified cross-validation scheme can be chosen.
  
  Parameters
  ----------
  x_ref
      Data used as reference distribution.
  model
      TensorFlow classification model used for drift detection.
  p_val
      p-value used for the significance of the test.
  x_ref_preprocessed
      Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only
      the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference
      data will also be preprocessed.
  preprocess_at_init
      Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference
      data will be preprocessed at prediction time. Only applies if `x_ref_preprocessed=False`.
  update_x_ref
      Reference data can optionally be updated to the last n instances seen by the detector
      or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while
      for reservoir sampling {'reservoir_sampling': n} is passed.
  preprocess_fn
      Function to preprocess the data before computing the data drift metrics.
  preds_type
      Whether the model outputs 'probs' or 'logits'.
  binarize_preds
      Whether to test for discrepency on soft (e.g. prob/log-prob) model predictions directly
      with a K-S test or binarise to 0-1 prediction errors and apply a binomial test.
  reg_loss_fn
      The regularisation term reg_loss_fn(model) is added to the loss function being optimized.
  train_size
      Optional fraction (float between 0 and 1) of the dataset used to train the classifier.
      The drift is detected on `1 - train_size`. Cannot be used in combination with `n_folds`.
  n_folds
      Optional number of stratified folds used for training. The model preds are then calculated
      on all the out-of-fold predictions. This allows to leverage all the reference and test data
      for drift detection at the expense of longer computation. If both `train_size` and `n_folds`
      are specified, `n_folds` is prioritized.
  retrain_from_scratch
      Whether the classifier should be retrained from scratch for each set of test data or whether
      it should instead continue training from where it left off on the previous set.
  seed
      Optional random seed for fold selection.
  optimizer
      Optimizer used during training of the classifier.
  learning_rate
      Learning rate used by optimizer.
  batch_size
      Batch size used during training of the classifier.
  preprocess_batch_fn
      Optional batch preprocessing function. For example to convert a list of objects to a batch which can be
      processed by the model.
  epochs
      Number of training epochs for the classifier for each (optional) fold.
  verbose
      Verbosity level during the training of the classifier.
      0 is silent, 1 a progress bar and 2 prints the statistics after each epoch.
  train_kwargs
      Optional additional kwargs when fitting the classifier.
  dataset
      Dataset object used during training.
  input_shape
      Shape of input data.
  data_type
      Optionally specify the data type (tabular, image or time-series). Added to metadata.
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code: def __init__(
      self,
      x_ref: np.ndarray,
      model: tf.keras.Model,
      p_val: float = 0.05,
      x_ref_preprocessed: bool = False,
      preprocess_at_init: bool = True,
      update_x_ref: Optional[Dict[str, int]] = None,
      preprocess_fn: Optional[Callable] = None,
      preds_type: str = "probs",
      binarize_preds: bool = False,
      reg_loss_fn: Callable = (lambda model: 0),
      train_size: Optional[float] = 0.75,
      n_folds: Optional[int] = None,
      retrain_from_scratch: bool = True,
      seed: int = 0,
      optimizer: OptimizerTF = tf.keras.optimizers.Adam,
      learning_rate: float = 1e-3,
      batch_size: int = 32,
      preprocess_batch_fn: Optional[Callable] = None,
      epochs: int = 3,
      verbose: int = 0,
      train_kwargs: Optional[dict] = None,
      dataset: Callable = TFDataset,
      input_shape: Optional[tuple] = None,
      data_type: Optional[str] = None,
  ) -> None:
      """
      Classifier-based drift detector. The classifier is trained on a fraction of the combined
      reference and test data and drift is detected on the remaining data. To use all the data
      to detect drift, a stratified cross-validation scheme can be chosen.
  
      Parameters
      ----------
      x_ref
          Data used as reference distribution.
      model
          TensorFlow classification model used for drift detection.
      p_val
          p-value used for the significance of the test.
      x_ref_preprocessed
          Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only
          the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference
          data will also be preprocessed.
      preprocess_at_init
          Whether to preprocess the reference data when the detector is instantiated. Otherwise, the reference
          data will be preprocessed at prediction time. Only applies if `x_ref_preprocessed=False`.
      update_x_ref
          Reference data can optionally be updated to the last n instances seen by the detector
          or via reservoir sampling with size n. For the former, the parameter equals {'last': n} while
          for reservoir sampling {'reservoir_sampling': n} is passed.
      preprocess_fn
          Function to preprocess the data before computing the data drift metrics.
      preds_type
          Whether the model outputs 'probs' or 'logits'.
      binarize_preds
          Whether to test for discrepency on soft (e.g. prob/log-prob) model predictions directly
          with a K-S test or binarise to 0-1 prediction errors and apply a binomial test.
      reg_loss_fn
          The regularisation term reg_loss_fn(model) is added to the loss function being optimized.
      train_size
          Optional fraction (float between 0 and 1) of the dataset used to train the classifier.
          The drift is detected on `1 - train_size`. Cannot be used in combination with `n_folds`.
      n_folds
          Optional number of stratified folds used for training. The model preds are then calculated
          on all the out-of-fold predictions. This allows to leverage all the reference and test data
          for drift detection at the expense of longer computation. If both `train_size` and `n_folds`
          are specified, `n_folds` is prioritized.
      retrain_from_scratch
          Whether the classifier should be retrained from scratch for each set of test data or whether
          it should instead continue training from where it left off on the previous set.
      seed
          Optional random seed for fold selection.
      optimizer
          Optimizer used during training of the classifier.
      learning_rate
          Learning rate used by optimizer.
      batch_size
          Batch size used during training of the classifier.
      preprocess_batch_fn
          Optional batch preprocessing function. For example to convert a list of objects to a batch which can be
          processed by the model.
      epochs
          Number of training epochs for the classifier for each (optional) fold.
      verbose
          Verbosity level during the training of the classifier.
          0 is silent, 1 a progress bar and 2 prints the statistics after each epoch.
      train_kwargs
          Optional additional kwargs when fitting the classifier.
      dataset
          Dataset object used during training.
      input_shape
          Shape of input data.
      data_type
          Optionally specify the data type (tabular, image or time-series). Added to metadata.
      """
      super().__init__(
          x_ref=x_ref,
          p_val=p_val,
          x_ref_preprocessed=x_ref_preprocessed,
          preprocess_at_init=preprocess_at_init,
          update_x_ref=update_x_ref,
          preprocess_fn=preprocess_fn,
          preds_type=preds_type,
          binarize_preds=binarize_preds,
          train_size=train_size,
          n_folds=n_folds,
          retrain_from_scratch=retrain_from_scratch,
          seed=seed,
          input_shape=input_shape,
          data_type=data_type,
      )
      if preds_type not in ["probs", "logits"]:
          raise ValueError("'preds_type' should be 'probs' or 'logits'")
  
      self.meta.update({"backend": Framework.TENSORFLOW.value})
  
      # define and compile classifier model
      self.original_model = model
      self.model = clone_model(model)
      self.loss_fn = BinaryCrossentropy(
          from_logits=(self.preds_type == "logits")
      )
      self.dataset = partial(
          dataset, batch_size=batch_size, shuffle=True
      )
      self.predict_fn = partial(
          predict_batch,
          preprocess_fn=preprocess_batch_fn,
          batch_size=batch_size,
      )
      optimizer = (
          optimizer(learning_rate=learning_rate)
          if isinstance(optimizer, type)
          else optimizer
      )
      self.train_kwargs = {
          "optimizer": optimizer,
          "epochs": epochs,
          "reg_loss_fn": reg_loss_fn,
          "preprocess_fn": preprocess_batch_fn,
          "verbose": verbose,
      }
      if isinstance(train_kwargs, dict):
          self.train_kwargs.update(train_kwargs)
  
file_path: alibi-detect/alibi_detect/cd/tensorflow/classifier.py
imports: from functools import partial
  import numpy
  import tensorflow
  from tensorflow.keras.losses import BinaryCrossentropy
  from scipy.special import softmax
  from typing import Callable, Dict, Optional, Tuple, Union
  from alibi_detect.cd.base import BaseClassifierDrift
  from alibi_detect.models.tensorflow.trainer import trainer
  from alibi_detect.utils.tensorflow.data import TFDataset
  from alibi_detect.utils.tensorflow.misc import clone_model
  from alibi_detect.utils.tensorflow.prediction import predict_batch
  from alibi_detect.utils.warnings import deprecated_alias
  from alibi_detect.utils.frameworks import Framework
  from alibi_detect.utils._types import OptimizerTF
label_desc: Use image node_id 1 to create a new ClassifierDriftTF object from inherited base classes: BaseClassifierDrift with example: obj = ClassifierDriftTF(x_ref, model, p_val, x_ref_preprocessed, preprocess_at_init, update_x_ref, preprocess_fn, preds_type, binarize_preds, reg_loss_fn, train_size, n_folds, retrain_from_scratch, seed, optimizer, learning_rate, batch_size, preprocess_batch_fn, epochs, verbose, train_kwargs, dataset, input_shape, data_type)